De-normalization involves combining or modifying tables in a database to improve query performance for data warehousing and decision support systems (DSS). It aims to enhance performance without losing information by bringing related data items closer together through techniques like collapsing tables, splitting tables, pre-joining tables, and adding redundant or derived columns. The level of de-normalization should be carefully considered based on a cost-benefit analysis of storage needs, maintenance issues, and query requirements.

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Lecture-7Lecture-7
De-normalizationDe-normalization
Mamuna Fatima

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Striking a balance between “good” & “evil”
Flat Table
Data Lists
Data Cubes 1st
Normal Form
2nd
Normal Form
3rd
Normal Form
4+ Normal Forms
NormalizationDe-normalization
One big flat file
Too many tables

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What is De-normalization?
the aim is to enhance performance without
loss of information.
 Normalization is a rule of thumb in DBMS,
but in DSS ease of use is achieved by way of
denormalization.
 De-normalization comes in many flavors,
such as combining tables, splitting tables,
adding data etc., but all done very carefully.

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 Bringing “close” dispersed but related data items.
 Very early studies showed performance difference in orders of
magnitude for different number de-normalized tables and rows
per table.
 The level of de-normalization should be carefully considered.
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Why De-normalization In DSS?

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How De-normalization improves performance?
De-normalization specifically improves
performance by either:
 Reducing the number of tables and hence the
reliance on joins, which consequently speeds up
performance.
 Reducing the number of joins required during
query execution, or
 Reducing the number of rows to be retrieved from
the Primary Data Table.

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4 Guidelines for De-normalization
1. Carefully do a cost-benefit analysis
(frequency of use, additional storage,
join time).
2. Do a data requirement and storage
analysis.
3. Weigh against the maintenance issue
of the redundant data (triggers used).
4. When in doubt, don’t denormalize.

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Areas for Applying De-Normalization Techniques
 Dealing with the abundance of star schemas.
 Fast access of time series data for analysis.
 Fast aggregate (sum, average etc.) results and
complicated calculations.
 Multidimensional analysis (e.g. geography) in a complex
hierarchy.
 Dealing with few updates but many join queries.
De-normalization will ultimately affect the database size and
query performance.

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Five principal De-normalization techniques
1. Collapsing Tables.
- Two entities with a One-to-One relationship.
- Two entities with a Many-to-Many relationship.
2. Splitting Tables (Horizontal/Vertical Splitting).
3. Pre-Joining.
4. Adding Redundant Columns (Reference Data).
5. Derived Attributes (Summary, Total, Balance etc).

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Collapsing Tables
ColA ColB
ColA ColC
normalized
ColA ColB ColC
denormalized
 Reduced storage space.
 Reduced update time.
 Does not changes business view.
 Reduced foreign keys.
 Reduced indexing.

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Collapsing Tables
ColA ColB
ColA ColC
normalized
ColA ColB ColC
denormalized
 Reduced storage space.
 Reduced update time.
 Does not changes business view.
 Reduced foreign keys.
 Reduced indexing.